Changing the Schedule of Medical Benefits and the Effect on Primary Care Physician Billing: Quasi-Experimental Evidence from Alberta.
Bibliographic record
Abstract
We exploit a quasi-experiment in the province of Alberta, Canada, to identify how changes in the schedule of medical benefits affected the provision of primary care services to patients with multiple co-morbidities. Specifically, Alberta introduced a new fee code to compensate physicians for completing a comprehensive annual care plan (CACP) for qualifying patients. During the period of study, primary care physicians could practice in two settings: (i) solo practice; or (ii) primary care networks (i.e., team based care). This paper asks how the policy change affected physician-billing patterns and whether delivery structure affected physician-billing. Data come from Alberta's administrative physician claims data, covering the full population of Alberta and all services provided by primary care physicians, for one year before and two years after the policy change. We employ a difference-in-differences methodology and implement a set of robustness checks to control for confounding from other contemporaneous changes that may have occurred in Alberta as well as unobserved physician heterogeneity. Our results suggest the new fee code became the sixth most billed code in its first year (totaling $17.9 million), but was billed by only a small proportion of physicians (roughly 2% of physicians accounted for 20% of total billings). The fee code was disproportionately billed by physicians in team-based care (PCNs), and increased the billing of other complementary fee codes by 5%-10% (or roughly $80 million). The results suggest the unintended consequences of a well-intentioned policy can be costly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".